Co-Designing a Consult Patient Decision Aid for Continuation Versus Deprescribing Cholinesterase Inhibitors in People Living with Dementia
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: As dementia progresses, people living with dementia may take high-risk, unnecessary, or ineffective medicines. Cholinesterase inhibitors (ChEIs) may have benefit in some people with dementia; however, up to one third are continued when no longer necessary or safe. Our aim was to co-design a consult patient decision aid (CPtDA) to support shared decision making between healthcare professionals and consumers about continuing or deprescribing ChEIs. METHODS: A systematic process was employed to design and test the CPtDA prototype. First, a steering group composed of healthcare professionals and a consumer representative was assembled. Guided by the International Patient Decision Aids Standards, the steering group defined the CPtDA's purpose, scope, and target audience and drafted the prototype for further testing. Interviews with consumers and healthcare professionals were conducted to gain feedback on the content, format, structure, comprehensibility and usability of the CPtDA prototype. RESULTS: After the steering group developed the CPtDA prototype, interviews were conducted with 11 consumers and six healthcare professionals. The content and format of the decision aid were improved iteratively over three rounds after consolidating the feedback at each round. The main changes included rewording the purpose of the decision aid and simplifying its layout and format. Participants reported that the decision aid is comprehensible and may be useful in practice. CONCLUSIONS: Limited available resources guide shared decision making about deprescribing. This study resulted in a co-designed and alpha-tested CPtDA for people living with dementia and carers to help them review the ongoing need for their ChEIs. Further research is needed to explore using the CPtDA in practice to support people living with dementia and their carers engage in the shared decision-making process about continuing or deprescribing their ChEIs. Our co-designed CPtDA could help people living with dementia and their carers review their goals of care alongside their healthcare professional. This may prompt conversations about appropriately using ChEIs and increase the uptake of deprescribing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".